Personalized Transcriptomic Analyses Identify Unique Signatures That Correlate with Genomic Subtypes in Acute Myeloid Leukemia (AML) Using Explainable Artificial Intelligence

Background

Multi-omic analysis can identify unique signatures that correlate with cancer subtypes. While clinically meaningful molecular subtypes of AML have been defined based on the status of single genes such as NPM1 and FLT3, such categories remain heterogeneous and further work is needed to characterize their genetic and transcriptomic diversity on a truly individualized basis. Further, patients (pts) with NPM1+/FLT3-ITD- AML have a better overall survival compared to patients with NPM1-/FLT3-ITD+, suggesting that these pts could have different transcriptomic signature that impact phenotype, pathophysiology, and outcomes. Many current transcriptome analytic techniques use clustering analysis to aggregate samples and look at relationships on a cohort-wide basis to build transcriptomic signatures that correlate with phenotype or outcome. Such approaches can undermine the heterogeneity of the gene expression in pts with the same signatures.

In this study, we took advantage of state of the art machine learning algorithms to identify unique transcriptomic signatures that correlate with AML genomic phenotype.

Methods

Genomic (whole exome sequencing and targeted deep sequencing) and transcriptomic data from 451 AML pts included in the Beat AML study (publicly available data) were used to build transcriptomic signatures that are specific for AML patients with NPM1+/FLT3-ITD+ compared to NPM1+/FLT3-ITD, and NPM1-/FLT3-ITD-. We chose these AML phenotypes as they have been described extensively and they correlate with clinical outcomes.

Results

A total of 242 patients (54%) had NPM1-/FLT3-, 35 (8%) were NPM1+/FLT3-, and 47 (10%) were NPM1+/FLT3+.

Our algorithm identified 20 genes that are highly specific for NPM1/FLT3ITD phenotype: HOXB-AS3, SCRN1, LMX1B, PCBD1, DNAJC15, HOXA3, NPTXq, RP11-1055B8, ABDH128, HOXB8, SOCS2, HOXB3, HOXB9, MIR503HG, FAM221B, NRP1, NDUFAF3, MEG3, CCDC136, and HIST1H2BC. Interestingly, several of those genes were overexpressed or underexpressed in specific phenotypes. For example, SCRN1, LMX1B, RP11-1055B8, ABDH128, HOXB8, MIR503HG, NRP1 are only overexpressed or underexpressed in patients with NPM1-/FLT3-, while PCBD1, NDUFAF3, FAM221B are overexpressed or underexpressed in pts with NPM1+/FLT3+. These genes affect several important pathways that regulate cell differentiation, proliferation, mitochondrial oxidative phosphorylation, histone modification and lipid metabolism. All these genes had previously been reported as having altered expression in genomic studies of AML, confirming our approach’s ability to identify biologically meaningful relationships. Further, our algorithm can provide a personalized explanation of overexpressed and underexpressed genes specific for a given patient, thus identifying targetable pathways for each pt. Figure 1 below shows three pts with the same genotype (NPM1+/FLT3-ITD+) but demonstrate different transcriptomic patterns of overexpression or underexpression that affect different biological pathways.

Conclusions

We describe the use of a state of the art explainable machine learning approach to define transcriptomic signatures that are specific for individual pts. In addition to correctly distinguishing AML subtype based on specific transcriptomic signatures, our model was able to accurately identify upregulated and downregulated genes that affecte several important biological pathways in AML and can summarize these pathways at an individual level. Such an approach can be used to provide personalized treatment options that can target the activated pathways at an individual level.